1. Field of the Invention
The present invention relates to an adaptive beamforming apparatus and method, and in particular to an improved weight vector update technique for the adaptive beamforming apparatus and method.
2. Background of the Related Art
In wireless communication systems, various diversity methods are used to increase the coverage area and capacity of a system. For example, use of a rake receiver architecture provides an effective immunity to the inter-symbol interference (ISI) in multipath propagation environments that cause the same signal to be repeatedly received at an antenna at a plurality of different time intervals.
Recently, directive antennas have been used to increase the signal-to-noise ratio (SNR) by increasing the energy radiated to a desired mobile terminal while simultaneously reducing the interference energy radiated to other remote mobile terminals. Such reduction in the interference energy radiated to the other mobile terminals can be achieved by generating spatially selective, directive transmission beam patterns.
One of the directive antenna techniques used to achieve such beam patterns is adaptive beamforming, in which the beam pattern produced by beamforming antenna arrays of the base station adapts in response to changing multipath conditions. In such beamforming arrays, the antenna beam pattern is generated so as to maximize signal energy transmitted to and received from an intended mobile terminal.
In order to adapt to the change of the multipath condition, each Angle of Departure (AOD) at which energy is to be transmitted from the base station antenna array to the intended mobile terminal must be determined. Each AOD is determined by estimating each Angle of Arrival (AOA) at the base station of signal energy from the mobile terminal. In the adaptive beamforming antenna systems, a weight vector concept is used to estimate an AOA spectrum corresponding to a desired AOD spectrum.
A Least Means Square (LMS) algorithm is one kind of adaptive beamforming algorithm, and uses only the pilot channel for transmitting a reference signal (non-blind beamforming algorithm).
In the LMS algorithm, the weight vector to minimize a mean square error is calculated using a pilot symbol as a training signal. The weight vector is calculated by the following equation 1 in the LMS algorithm.                                                         w              k                        ⁡                          (                              m                +                1                            )                                =                                                    w                k                            ⁡                              (                m                )                                      -                          μ              ⁢                                                           ⁢                                                                                          r                      DPCCH_k                                        ⁡                                          (                      m                      )                                                        ⁡                                      [                                                                                            d                                                      k                            ,                            c                                                                          ⁡                                                  (                          m                          )                                                                    -                                                                                                    w                            k                            H                                                    ⁡                                                      (                            m                            )                                                                          ⁢                                                                              r                            DPCCH_k                                                    ⁡                                                      (                            m                            )                                                                                                                ]                                                  H                                                    ⁢                                  ⁢                                            r              DPCCH_K                        ⁡                          (              m              )                                =                                    [                                                                    r                    DPCCH_k                    0                                    ⁡                                      (                    m                    )                                                  ⁢                                                      r                    DPCCH_k                    1                                    ⁡                                      (                    m                    )                                                  ⁢                                                                   ⁢                …                ⁢                                                                   ⁢                                                      r                    DPCCH_k                                          (                                              P                        -                        1                                            )                                                        ⁡                                      (                    m                    )                                                              ]                        H                          ⁢                                  ⁢                                            w              k                        ⁡                          (              m              )                                =                                    [                                                                    w                    k                                          (                      0                      )                                                        ⁡                                      (                    m                    )                                                  ⁢                                                      w                    k                    1                                    ⁡                                      (                    m                    )                                                  ⁢                                                                   ⁢                …                ⁢                                                                   ⁢                                                      w                    k                                          (                                              P                        -                        1                                            )                                                        ⁡                                      (                    m                    )                                                              ]                        H                                              〈                  Equation          ⁢                                           ⁢          1                〉            where w is weight vector, and, is a weight vector update coefficient.
Another adaptive beamforming algorithm is the Constant Modulus Algorithm (CMA). The CMA is a blind adaptive beamforming algorithm that uses a constant envelope signal rather than the training signal. This means that there is no intended amplitude modulation. In the CMA, the weight vector is calculated by the following equation 2.                                                         y              DPCCH_k                        ⁡                          (              m              )                                =                                                    w                k                H                            ⁡                              (                m                )                                      ⁢                                          r                MPCCH_k                            ⁡                              (                m                )                                                    ⁢                                  ⁢                                            e              DPCCH_k                        ⁡                          (              m              )                                =                      2            ⁢                          (                                                                    y                    DPCCH_k                                    ⁡                                      (                    m                    )                                                  -                                                                            y                      DPCCH_k                                        ⁡                                          (                      m                      )                                                                            |                                          y                                              DPCCH_k                        ⁢                                                  (                          m                          )                                                                                      |                                                              )                                      ⁢                                  ⁢                                            w              k                        ⁡                          (                              m                +                1                            )                                =                                                    w                k                            ⁡                              (                m                )                                      -                          μ              ⁢                                                           ⁢                                                r                  DPCCH_k                                ⁡                                  (                  m                  )                                            ⁢                                                e                  DPCCH_k                  *                                ⁡                                  (                  m                  )                                                                                        〈                  Equation          ⁢                                           ⁢          2                〉            
The related art adaptive beamforming methods have various problems. For example, the LMS algorithm converges to an optimal value slowly. Hence, it is difficult to employ the LMS algorithm in fast fading radio environments. Additionally, with regard to CMA, since it is a blind adaptive algorithm, its convergence speed is slower than those algorithms that use the training signals. Also, the convergence characteristics of the CMA are not precisely defined relative to the LMS algorithm.
Even though there exist various other beamforming algorithms, most of them are much too complex to apply to the radio systems, as compared to the LMS and CMA. Accordingly, such algorithms are problematic.
The above references are incorporated by reference herein where appropriate for appropriate teachings of additional or alternative details, features and/or technical background.